Overview

Dataset statistics

Number of variables56
Number of observations319952
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory136.7 MiB
Average record size in memory448.0 B

Variable types

Numeric8
Categorical41
Text7

Alerts

NPCEP8A is highly imbalanced (61.7%)Imbalance
NPCEP9A is highly imbalanced (59.9%)Imbalance
NPCEP10 is highly imbalanced (58.1%)Imbalance
NPCEP11AA is highly imbalanced (60.4%)Imbalance
NPCEP13 is highly imbalanced (50.3%)Imbalance
NPCEP13A is highly imbalanced (87.9%)Imbalance
NPCEP15 is highly imbalanced (83.8%)Imbalance
NPCEP16A is highly imbalanced (83.0%)Imbalance
NPCEP16B is highly imbalanced (79.5%)Imbalance
NPCEP16C is highly imbalanced (95.6%)Imbalance
NPCEP16D is highly imbalanced (93.0%)Imbalance
NPCEP16E is highly imbalanced (99.1%)Imbalance
NPCEP16F is highly imbalanced (99.3%)Imbalance
NPCEP16G is highly imbalanced (99.8%)Imbalance
NPCEP16H is highly imbalanced (99.7%)Imbalance
NPCEP16I is highly imbalanced (99.9%)Imbalance
NPCEP16J is highly imbalanced (99.5%)Imbalance
NPCEP16K is highly imbalanced (90.4%)Imbalance
NPCEP16AA is highly imbalanced (80.4%)Imbalance
NPCEP16AB is highly imbalanced (92.6%)Imbalance
NPCEP16B1 is highly imbalanced (83.1%)Imbalance
NPCEP19 is highly imbalanced (97.8%)Imbalance
NPCEP21A is highly imbalanced (72.8%)Imbalance
NPCEP22A is highly imbalanced (70.8%)Imbalance
NPCEP24A is highly imbalanced (60.2%)Imbalance
NPCEP25A is highly imbalanced (71.1%)Imbalance
NPCEP27 is highly imbalanced (58.0%)Imbalance
DIRECTORIO_PER has unique valuesUnique

Reproduction

Analysis started2024-05-07 04:51:03.781078
Analysis finished2024-05-07 04:51:37.849049
Duration34.07 seconds
Software versionydata-profiling vv4.6.5
Download configurationconfig.json

Variables

DIRECTORIO_PER
Real number (ℝ)

UNIQUE 

Distinct319952
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19682295
Minimum10100011
Maximum3.1754311 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2024-05-06T23:51:37.949349image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum10100011
5-th percentile10913314
Q114570212
median18186114
Q324850312
95-th percentile29416912
Maximum3.1754311 × 108
Range3.074431 × 108
Interquartile range (IQR)10280100

Descriptive statistics

Standard deviation7563170.8
Coefficient of variation (CV)0.38426264
Kurtosis290.38191
Mean19682295
Median Absolute Deviation (MAD)4765449.5
Skewness10.43723
Sum6.2973897 × 1012
Variance5.7201552 × 1013
MonotonicityNot monotonic
2024-05-06T23:51:38.085998image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10100011 1
 
< 0.1%
23193513 1
 
< 0.1%
23194413 1
 
< 0.1%
23194412 1
 
< 0.1%
23194411 1
 
< 0.1%
23193714 1
 
< 0.1%
23193713 1
 
< 0.1%
23193712 1
 
< 0.1%
23193711 1
 
< 0.1%
23193512 1
 
< 0.1%
Other values (319942) 319942
> 99.9%
ValueCountFrequency (%)
10100011 1
< 0.1%
10100012 1
< 0.1%
10100013 1
< 0.1%
10100111 1
< 0.1%
10100112 1
< 0.1%
10100113 1
< 0.1%
10100114 1
< 0.1%
10100211 1
< 0.1%
10100212 1
< 0.1%
10100311 1
< 0.1%
ValueCountFrequency (%)
317543112 1
< 0.1%
317543111 1
< 0.1%
317543110 1
< 0.1%
317463110 1
< 0.1%
315231110 1
< 0.1%
315230110 1
< 0.1%
294700110 1
< 0.1%
291262110 1
< 0.1%
287404110 1
< 0.1%
281937110 1
< 0.1%

DIRECTORIO_HOG
Real number (ℝ)

Distinct109111
Distinct (%)34.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1955277.1
Minimum1010001
Maximum3178851
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2024-05-06T23:51:38.232736image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1010001
5-th percentile1091276.5
Q11456781
median1818101
Q32483603.5
95-th percentile2940691
Maximum3178851
Range2168850
Interquartile range (IQR)1026822.5

Descriptive statistics

Standard deviation586867.83
Coefficient of variation (CV)0.30014561
Kurtosis-1.1519761
Mean1955277.1
Median Absolute Deviation (MAD)475920
Skewness0.26116389
Sum6.2559481 × 1011
Variance3.4441385 × 1011
MonotonicityIncreasing
2024-05-06T23:51:38.373563image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1409051 17
 
< 0.1%
1091071 14
 
< 0.1%
1857011 14
 
< 0.1%
1692321 14
 
< 0.1%
1590861 14
 
< 0.1%
1120141 13
 
< 0.1%
2803081 13
 
< 0.1%
1473411 13
 
< 0.1%
1294831 13
 
< 0.1%
1353501 13
 
< 0.1%
Other values (109101) 319814
> 99.9%
ValueCountFrequency (%)
1010001 3
< 0.1%
1010011 4
< 0.1%
1010021 2
 
< 0.1%
1010031 3
< 0.1%
1010041 1
 
< 0.1%
1010051 1
 
< 0.1%
1010061 1
 
< 0.1%
1010071 4
< 0.1%
1010081 5
< 0.1%
1010082 3
< 0.1%
ValueCountFrequency (%)
3178851 2
 
< 0.1%
3178811 2
 
< 0.1%
3178741 1
 
< 0.1%
3178591 1
 
< 0.1%
3178441 2
 
< 0.1%
3178351 4
< 0.1%
3178341 2
 
< 0.1%
3178321 2
 
< 0.1%
3178311 3
< 0.1%
3178251 5
< 0.1%

DIRECTORIO
Real number (ℝ)

Distinct107218
Distinct (%)33.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean195527.6
Minimum101000
Maximum317885
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2024-05-06T23:51:38.507512image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum101000
5-th percentile109127.55
Q1145678
median181810
Q3248360.25
95-th percentile294069
Maximum317885
Range216885
Interquartile range (IQR)102682.25

Descriptive statistics

Standard deviation58686.783
Coefficient of variation (CV)0.30014577
Kurtosis-1.1519761
Mean195527.6
Median Absolute Deviation (MAD)47592
Skewness0.26116388
Sum6.2559448 × 1010
Variance3.4441385 × 109
MonotonicityIncreasing
2024-05-06T23:51:38.643209image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
184980 22
 
< 0.1%
145788 22
 
< 0.1%
112379 21
 
< 0.1%
172991 19
 
< 0.1%
135993 19
 
< 0.1%
145803 18
 
< 0.1%
140905 17
 
< 0.1%
111041 17
 
< 0.1%
104460 16
 
< 0.1%
112477 16
 
< 0.1%
Other values (107208) 319765
99.9%
ValueCountFrequency (%)
101000 3
 
< 0.1%
101001 4
< 0.1%
101002 2
 
< 0.1%
101003 3
 
< 0.1%
101004 1
 
< 0.1%
101005 1
 
< 0.1%
101006 1
 
< 0.1%
101007 4
< 0.1%
101008 8
< 0.1%
101009 5
< 0.1%
ValueCountFrequency (%)
317885 2
 
< 0.1%
317881 2
 
< 0.1%
317874 1
 
< 0.1%
317859 1
 
< 0.1%
317844 2
 
< 0.1%
317835 4
< 0.1%
317834 2
 
< 0.1%
317832 2
 
< 0.1%
317831 3
< 0.1%
317825 5
< 0.1%

SECUENCIA_P
Real number (ℝ)

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0194842
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2024-05-06T23:51:38.747956image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum9
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.17583773
Coefficient of variation (CV)0.17247716
Kurtosis199.18062
Mean1.0194842
Median Absolute Deviation (MAD)0
Skewness12.069991
Sum326186
Variance0.030918908
MonotonicityNot monotonic
2024-05-06T23:51:38.855070image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 315132
98.5%
2 3754
 
1.2%
3 805
 
0.3%
4 199
 
0.1%
5 48
 
< 0.1%
6 7
 
< 0.1%
7 4
 
< 0.1%
8 2
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
1 315132
98.5%
2 3754
 
1.2%
3 805
 
0.3%
4 199
 
0.1%
5 48
 
< 0.1%
6 7
 
< 0.1%
7 4
 
< 0.1%
8 2
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
9 1
 
< 0.1%
8 2
 
< 0.1%
7 4
 
< 0.1%
6 7
 
< 0.1%
5 48
 
< 0.1%
4 199
 
0.1%
3 805
 
0.3%
2 3754
 
1.2%
1 315132
98.5%

ORDEN
Real number (ℝ)

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.333525
Minimum1
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2024-05-06T23:51:38.958279image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile5
Maximum17
Range16
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3719547
Coefficient of variation (CV)0.58793229
Kurtosis2.4552737
Mean2.333525
Median Absolute Deviation (MAD)1
Skewness1.2729511
Sum746616
Variance1.8822597
MonotonicityNot monotonic
2024-05-06T23:51:39.073322image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1 109111
34.1%
2 90132
28.2%
3 62482
19.5%
4 35646
 
11.1%
5 14103
 
4.4%
6 5101
 
1.6%
7 1980
 
0.6%
8 801
 
0.3%
9 339
 
0.1%
10 142
 
< 0.1%
Other values (7) 115
 
< 0.1%
ValueCountFrequency (%)
1 109111
34.1%
2 90132
28.2%
3 62482
19.5%
4 35646
 
11.1%
5 14103
 
4.4%
6 5101
 
1.6%
7 1980
 
0.6%
8 801
 
0.3%
9 339
 
0.1%
10 142
 
< 0.1%
ValueCountFrequency (%)
17 1
 
< 0.1%
16 1
 
< 0.1%
15 1
 
< 0.1%
14 5
 
< 0.1%
13 13
 
< 0.1%
12 30
 
< 0.1%
11 64
 
< 0.1%
10 142
 
< 0.1%
9 339
0.1%
8 801
0.3%

NPCEP4
Real number (ℝ)

Distinct108
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.573417
Minimum0
Maximum107
Zeros3101
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2024-05-06T23:51:39.204994image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q118
median34
Q352
95-th percentile73
Maximum107
Range107
Interquartile range (IQR)34

Descriptive statistics

Standard deviation21.347928
Coefficient of variation (CV)0.60010899
Kurtosis-0.72245541
Mean35.573417
Median Absolute Deviation (MAD)17
Skewness0.32342443
Sum11381786
Variance455.73401
MonotonicityNot monotonic
2024-05-06T23:51:39.342851image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23 5733
 
1.8%
22 5725
 
1.8%
25 5601
 
1.8%
21 5553
 
1.7%
30 5534
 
1.7%
20 5514
 
1.7%
35 5397
 
1.7%
24 5373
 
1.7%
27 5313
 
1.7%
40 5294
 
1.7%
Other values (98) 264915
82.8%
ValueCountFrequency (%)
0 3101
1.0%
1 3612
1.1%
2 3719
1.2%
3 3742
1.2%
4 3955
1.2%
5 3923
1.2%
6 3924
1.2%
7 4149
1.3%
8 4275
1.3%
9 4370
1.4%
ValueCountFrequency (%)
107 1
 
< 0.1%
106 2
 
< 0.1%
105 1
 
< 0.1%
104 1
 
< 0.1%
103 1
 
< 0.1%
102 3
 
< 0.1%
101 9
 
< 0.1%
100 11
< 0.1%
99 20
< 0.1%
98 26
< 0.1%

NPCEP5
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
2
169263 
1
150670 
3
 
19

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters319952
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row2
4th row1
5th row2

Common Values

ValueCountFrequency (%)
2 169263
52.9%
1 150670
47.1%
3 19
 
< 0.1%

Length

2024-05-06T23:51:39.481857image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:51:39.597330image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2 169263
52.9%
1 150670
47.1%
3 19
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
2 169263
52.9%
1 150670
47.1%
3 19
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 319952
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 169263
52.9%
1 150670
47.1%
3 19
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 319952
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 169263
52.9%
1 150670
47.1%
3 19
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 319952
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 169263
52.9%
1 150670
47.1%
3 19
 
< 0.1%

NPCEP6
Real number (ℝ)

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5451661
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2024-05-06T23:51:39.704029image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile7
Maximum14
Range13
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9287395
Coefficient of variation (CV)0.75780496
Kurtosis11.180038
Mean2.5451661
Median Absolute Deviation (MAD)1
Skewness2.8157097
Sum814331
Variance3.7200362
MonotonicityNot monotonic
2024-05-06T23:51:39.816731image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
3 112421
35.1%
1 109111
34.1%
2 59928
18.7%
4 14828
 
4.6%
9 5753
 
1.8%
7 5614
 
1.8%
5 4966
 
1.6%
8 2844
 
0.9%
14 2495
 
0.8%
6 1364
 
0.4%
Other values (4) 628
 
0.2%
ValueCountFrequency (%)
1 109111
34.1%
2 59928
18.7%
3 112421
35.1%
4 14828
 
4.6%
5 4966
 
1.6%
6 1364
 
0.4%
7 5614
 
1.8%
8 2844
 
0.9%
9 5753
 
1.8%
10 341
 
0.1%
ValueCountFrequency (%)
14 2495
0.8%
13 204
 
0.1%
12 43
 
< 0.1%
11 40
 
< 0.1%
10 341
 
0.1%
9 5753
1.8%
8 2844
0.9%
7 5614
1.8%
6 1364
 
0.4%
5 4966
1.6%

NPCEP7
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
5
117639 
6
70622 
2
53629 
38770 
4
19256 
Other values (2)
20036 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters319952
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row6
2nd row6
3rd row5
4th row6
5th row6

Common Values

ValueCountFrequency (%)
5 117639
36.8%
6 70622
22.1%
2 53629
16.8%
38770
 
12.1%
4 19256
 
6.0%
3 13273
 
4.1%
1 6763
 
2.1%

Length

2024-05-06T23:51:40.167419image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:51:40.281666image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
5 117639
41.8%
6 70622
25.1%
2 53629
19.1%
4 19256
 
6.8%
3 13273
 
4.7%
1 6763
 
2.4%

Most occurring characters

ValueCountFrequency (%)
5 117639
36.8%
6 70622
22.1%
2 53629
16.8%
38770
 
12.1%
4 19256
 
6.0%
3 13273
 
4.1%
1 6763
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 281182
87.9%
Space Separator 38770
 
12.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 117639
41.8%
6 70622
25.1%
2 53629
19.1%
4 19256
 
6.8%
3 13273
 
4.7%
1 6763
 
2.4%
Space Separator
ValueCountFrequency (%)
38770
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 319952
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 117639
36.8%
6 70622
22.1%
2 53629
16.8%
38770
 
12.1%
4 19256
 
6.0%
3 13273
 
4.1%
1 6763
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 319952
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 117639
36.8%
6 70622
22.1%
2 53629
16.8%
38770
 
12.1%
4 19256
 
6.0%
3 13273
 
4.1%
1 6763
 
2.1%

NPCEP8
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
188938 
1
127161 
2
 
3853

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters319952
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row
4th row1
5th row1

Common Values

ValueCountFrequency (%)
188938
59.1%
1 127161
39.7%
2 3853
 
1.2%

Length

2024-05-06T23:51:40.408242image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:51:40.501276image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 127161
97.1%
2 3853
 
2.9%

Most occurring characters

ValueCountFrequency (%)
188938
59.1%
1 127161
39.7%
2 3853
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Space Separator 188938
59.1%
Decimal Number 131014
40.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 127161
97.1%
2 3853
 
2.9%
Space Separator
ValueCountFrequency (%)
188938
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 319952
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
188938
59.1%
1 127161
39.7%
2 3853
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 319952
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
188938
59.1%
1 127161
39.7%
2 3853
 
1.2%

NPCEP8A
Categorical

IMBALANCE 

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
192791 
2
61224 
1
59918 
3
 
2613
4
 
1752
Other values (10)
 
1654

Length

Max length2
Median length1
Mean length1.0000813
Min length1

Characters and Unicode

Total characters319978
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row2
2nd row1
3rd row
4th row2
5th row1

Common Values

ValueCountFrequency (%)
192791
60.3%
2 61224
 
19.1%
1 59918
 
18.7%
3 2613
 
0.8%
4 1752
 
0.5%
5 862
 
0.3%
6 440
 
0.1%
7 193
 
0.1%
8 87
 
< 0.1%
9 46
 
< 0.1%
Other values (5) 26
 
< 0.1%

Length

2024-05-06T23:51:40.602092image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2 61224
48.1%
1 59918
47.1%
3 2613
 
2.1%
4 1752
 
1.4%
5 862
 
0.7%
6 440
 
0.3%
7 193
 
0.2%
8 87
 
0.1%
9 46
 
< 0.1%
10 15
 
< 0.1%
Other values (4) 11
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
192791
60.3%
2 61228
 
19.1%
1 59949
 
18.7%
3 2613
 
0.8%
4 1752
 
0.5%
5 863
 
0.3%
6 441
 
0.1%
7 193
 
0.1%
8 87
 
< 0.1%
9 46
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator 192791
60.3%
Decimal Number 127187
39.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 61228
48.1%
1 59949
47.1%
3 2613
 
2.1%
4 1752
 
1.4%
5 863
 
0.7%
6 441
 
0.3%
7 193
 
0.2%
8 87
 
0.1%
9 46
 
< 0.1%
0 15
 
< 0.1%
Space Separator
ValueCountFrequency (%)
192791
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 319978
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
192791
60.3%
2 61228
 
19.1%
1 59949
 
18.7%
3 2613
 
0.8%
4 1752
 
0.5%
5 863
 
0.3%
6 441
 
0.1%
7 193
 
0.1%
8 87
 
< 0.1%
9 46
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 319978
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
192791
60.3%
2 61228
 
19.1%
1 59949
 
18.7%
3 2613
 
0.8%
4 1752
 
0.5%
5 863
 
0.3%
6 441
 
0.1%
7 193
 
0.1%
8 87
 
< 0.1%
9 46
 
< 0.1%

NPCEP9
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
2
213237 
3
99786 
1
 
6929

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters319952
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 213237
66.6%
3 99786
31.2%
1 6929
 
2.2%

Length

2024-05-06T23:51:40.705174image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:51:40.795835image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2 213237
66.6%
3 99786
31.2%
1 6929
 
2.2%

Most occurring characters

ValueCountFrequency (%)
2 213237
66.6%
3 99786
31.2%
1 6929
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 319952
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 213237
66.6%
3 99786
31.2%
1 6929
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
Common 319952
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 213237
66.6%
3 99786
31.2%
1 6929
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 319952
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 213237
66.6%
3 99786
31.2%
1 6929
 
2.2%

NPCEP9A
Categorical

IMBALANCE 

Distinct34
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
220166 
25
26980 
15
 
15577
11
 
10785
73
 
9775
Other values (29)
36669 

Length

Max length2
Median length1
Mean length1.311878
Min length1

Characters and Unicode

Total characters419738
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row15
2nd row15
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
220166
68.8%
25 26980
 
8.4%
15 15577
 
4.9%
11 10785
 
3.4%
73 9775
 
3.1%
68 5819
 
1.8%
17 3218
 
1.0%
05 3154
 
1.0%
41 3007
 
0.9%
76 2886
 
0.9%
Other values (24) 18585
 
5.8%

Length

2024-05-06T23:51:40.902893image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
25 26980
27.0%
15 15577
15.6%
11 10785
 
10.8%
73 9775
 
9.8%
68 5819
 
5.8%
17 3218
 
3.2%
05 3154
 
3.2%
41 3007
 
3.0%
76 2886
 
2.9%
50 2131
 
2.1%
Other values (23) 16454
16.5%

Most occurring characters

ValueCountFrequency (%)
220166
52.5%
5 51289
 
12.2%
1 47183
 
11.2%
2 31199
 
7.4%
7 18425
 
4.4%
3 13869
 
3.3%
6 11548
 
2.8%
8 9366
 
2.2%
0 8918
 
2.1%
4 6453
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Space Separator 220166
52.5%
Decimal Number 199572
47.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 51289
25.7%
1 47183
23.6%
2 31199
15.6%
7 18425
 
9.2%
3 13869
 
6.9%
6 11548
 
5.8%
8 9366
 
4.7%
0 8918
 
4.5%
4 6453
 
3.2%
9 1322
 
0.7%
Space Separator
ValueCountFrequency (%)
220166
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 419738
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
220166
52.5%
5 51289
 
12.2%
1 47183
 
11.2%
2 31199
 
7.4%
7 18425
 
4.4%
3 13869
 
3.3%
6 11548
 
2.8%
8 9366
 
2.2%
0 8918
 
2.1%
4 6453
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 419738
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
220166
52.5%
5 51289
 
12.2%
1 47183
 
11.2%
2 31199
 
7.4%
7 18425
 
4.4%
3 13869
 
3.3%
6 11548
 
2.8%
8 9366
 
2.2%
0 8918
 
2.1%
4 6453
 
1.5%
Distinct1100
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
2024-05-06T23:51:41.164777image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length5
Median length1
Mean length2.2475121
Min length1

Characters and Unicode

Total characters719096
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36 ?
Unique (%)< 0.1%

Sample

1st row15842
2nd row15842
3rd row
4th row
5th row
ValueCountFrequency (%)
11001 10784
 
10.8%
73001 2029
 
2.0%
15001 1525
 
1.5%
68001 1426
 
1.4%
05001 1411
 
1.4%
08001 1367
 
1.4%
76001 1340
 
1.3%
25899 1066
 
1.1%
50001 1059
 
1.1%
41001 1045
 
1.0%
Other values (1089) 76734
76.9%
2024-05-06T23:51:41.613009image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
220166
30.6%
1 98025
13.6%
0 87923
 
12.2%
5 72453
 
10.1%
2 53700
 
7.5%
7 41514
 
5.8%
3 37030
 
5.1%
8 32816
 
4.6%
6 31941
 
4.4%
4 26088
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 498930
69.4%
Space Separator 220166
30.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 98025
19.6%
0 87923
17.6%
5 72453
14.5%
2 53700
10.8%
7 41514
8.3%
3 37030
 
7.4%
8 32816
 
6.6%
6 31941
 
6.4%
4 26088
 
5.2%
9 17440
 
3.5%
Space Separator
ValueCountFrequency (%)
220166
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 719096
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
220166
30.6%
1 98025
13.6%
0 87923
 
12.2%
5 72453
 
10.1%
2 53700
 
7.5%
7 41514
 
5.8%
3 37030
 
5.1%
8 32816
 
4.6%
6 31941
 
4.4%
4 26088
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 719096
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
220166
30.6%
1 98025
13.6%
0 87923
 
12.2%
5 72453
 
10.1%
2 53700
 
7.5%
7 41514
 
5.8%
3 37030
 
5.1%
8 32816
 
4.6%
6 31941
 
4.4%
4 26088
 
3.6%

NPCEP10
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
1
275897 
2
37126 
 
6929

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters319952
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 275897
86.2%
2 37126
 
11.6%
6929
 
2.2%

Length

2024-05-06T23:51:41.770750image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:51:41.862351image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 275897
88.1%
2 37126
 
11.9%

Most occurring characters

ValueCountFrequency (%)
1 275897
86.2%
2 37126
 
11.6%
6929
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 313023
97.8%
Space Separator 6929
 
2.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 275897
88.1%
2 37126
 
11.9%
Space Separator
ValueCountFrequency (%)
6929
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 319952
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 275897
86.2%
2 37126
 
11.6%
6929
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 319952
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 275897
86.2%
2 37126
 
11.6%
6929
 
2.2%

NPCEP11A
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
1
215200 
2
101203 
3
 
3549

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters319952
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 215200
67.3%
2 101203
31.6%
3 3549
 
1.1%

Length

2024-05-06T23:51:41.973393image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:51:42.070959image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 215200
67.3%
2 101203
31.6%
3 3549
 
1.1%

Most occurring characters

ValueCountFrequency (%)
1 215200
67.3%
2 101203
31.6%
3 3549
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 319952
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 215200
67.3%
2 101203
31.6%
3 3549
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common 319952
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 215200
67.3%
2 101203
31.6%
3 3549
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 319952
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 215200
67.3%
2 101203
31.6%
3 3549
 
1.1%

NPCEP11AA
Categorical

IMBALANCE 

Distinct34
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
218749 
25
29657 
11
 
16579
15
 
13663
73
 
8558
Other values (29)
32746 

Length

Max length2
Median length1
Mean length1.3024235
Min length1

Characters and Unicode

Total characters416713
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row15
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
218749
68.4%
25 29657
 
9.3%
11 16579
 
5.2%
15 13663
 
4.3%
73 8558
 
2.7%
68 5066
 
1.6%
17 2894
 
0.9%
5 2844
 
0.9%
76 2623
 
0.8%
41 2564
 
0.8%
Other values (24) 16755
 
5.2%

Length

2024-05-06T23:51:42.176358image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
25 29657
29.3%
11 16579
16.4%
15 13663
13.5%
73 8558
 
8.5%
68 5066
 
5.0%
17 2894
 
2.9%
5 2844
 
2.8%
76 2623
 
2.6%
41 2564
 
2.5%
50 2190
 
2.2%
Other values (23) 14565
14.4%

Most occurring characters

ValueCountFrequency (%)
218749
52.5%
1 55634
 
13.4%
5 51389
 
12.3%
2 33322
 
8.0%
7 16347
 
3.9%
3 12120
 
2.9%
6 10216
 
2.5%
8 8266
 
2.0%
4 5627
 
1.4%
0 3885
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Space Separator 218749
52.5%
Decimal Number 197964
47.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 55634
28.1%
5 51389
26.0%
2 33322
16.8%
7 16347
 
8.3%
3 12120
 
6.1%
6 10216
 
5.2%
8 8266
 
4.2%
4 5627
 
2.8%
0 3885
 
2.0%
9 1158
 
0.6%
Space Separator
ValueCountFrequency (%)
218749
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 416713
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
218749
52.5%
1 55634
 
13.4%
5 51389
 
12.3%
2 33322
 
8.0%
7 16347
 
3.9%
3 12120
 
2.9%
6 10216
 
2.5%
8 8266
 
2.0%
4 5627
 
1.4%
0 3885
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 416713
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
218749
52.5%
1 55634
 
13.4%
5 51389
 
12.3%
2 33322
 
8.0%
7 16347
 
3.9%
3 12120
 
2.9%
6 10216
 
2.5%
8 8266
 
2.0%
4 5627
 
1.4%
0 3885
 
0.9%
Distinct1092
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
2024-05-06T23:51:42.417816image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length5
Median length1
Mean length2.251344
Min length1

Characters and Unicode

Total characters720322
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36 ?
Unique (%)< 0.1%

Sample

1st row15842
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
11001 16579
 
16.4%
25899 3098
 
3.1%
25269 2113
 
2.1%
73001 1810
 
1.8%
25175 1455
 
1.4%
15001 1394
 
1.4%
8001 1269
 
1.3%
5001 1266
 
1.3%
25843 1239
 
1.2%
68001 1231
 
1.2%
Other values (1081) 69749
68.9%
2024-05-06T23:51:42.836146image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
218749
30.4%
1 109208
15.2%
0 88811
12.3%
5 71306
 
9.9%
2 54384
 
7.5%
7 37917
 
5.3%
3 33546
 
4.7%
8 31968
 
4.4%
6 29599
 
4.1%
4 23744
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 501573
69.6%
Space Separator 218749
30.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 109208
21.8%
0 88811
17.7%
5 71306
14.2%
2 54384
10.8%
7 37917
 
7.6%
3 33546
 
6.7%
8 31968
 
6.4%
6 29599
 
5.9%
4 23744
 
4.7%
9 21090
 
4.2%
Space Separator
ValueCountFrequency (%)
218749
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 720322
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
218749
30.4%
1 109208
15.2%
0 88811
12.3%
5 71306
 
9.9%
2 54384
 
7.5%
7 37917
 
5.3%
3 33546
 
4.7%
8 31968
 
4.4%
6 29599
 
4.1%
4 23744
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 720322
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
218749
30.4%
1 109208
15.2%
0 88811
12.3%
5 71306
 
9.9%
2 54384
 
7.5%
7 37917
 
5.3%
3 33546
 
4.7%
8 31968
 
4.4%
6 29599
 
4.1%
4 23744
 
3.3%
Distinct198
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
2024-05-06T23:51:43.039712image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length28
Median length1
Mean length1.0850159
Min length1

Characters and Unicode

Total characters347153
Distinct characters48
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique113 ?
Unique (%)< 0.1%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
venezuela 2320
60.6%
estados 135
 
3.5%
unidos 134
 
3.5%
ecuador 130
 
3.4%
espana 94
 
2.5%
francia 63
 
1.6%
argentina 55
 
1.4%
mexico 55
 
1.4%
alemania 54
 
1.4%
brasil 50
 
1.3%
Other values (183) 736
 
19.2%
2024-05-06T23:51:43.352769image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
316682
91.2%
E 7823
 
2.3%
A 4075
 
1.2%
N 3028
 
0.9%
U 2898
 
0.8%
L 2676
 
0.8%
V 2393
 
0.7%
Z 2357
 
0.7%
I 809
 
0.2%
S 742
 
0.2%
Other values (38) 3670
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator 316682
91.2%
Uppercase Letter 30362
 
8.7%
Lowercase Letter 82
 
< 0.1%
Control 10
 
< 0.1%
Other Punctuation 8
 
< 0.1%
Decimal Number 6
 
< 0.1%
Dash Punctuation 3
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 7823
25.8%
A 4075
13.4%
N 3028
 
10.0%
U 2898
 
9.5%
L 2676
 
8.8%
V 2393
 
7.9%
Z 2357
 
7.8%
I 809
 
2.7%
S 742
 
2.4%
O 657
 
2.2%
Other values (17) 2904
 
9.6%
Lowercase Letter
ValueCountFrequency (%)
a 16
19.5%
e 13
15.9%
u 8
9.8%
l 7
8.5%
i 7
8.5%
n 7
8.5%
s 6
 
7.3%
z 4
 
4.9%
r 3
 
3.7%
o 3
 
3.7%
Other values (4) 8
9.8%
Control
ValueCountFrequency (%)
‘ 9
90.0%
š 1
 
10.0%
Other Punctuation
ValueCountFrequency (%)
. 5
62.5%
, 3
37.5%
Space Separator
ValueCountFrequency (%)
316682
100.0%
Decimal Number
ValueCountFrequency (%)
9 6
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 316709
91.2%
Latin 30444
 
8.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 7823
25.7%
A 4075
13.4%
N 3028
 
9.9%
U 2898
 
9.5%
L 2676
 
8.8%
V 2393
 
7.9%
Z 2357
 
7.7%
I 809
 
2.7%
S 742
 
2.4%
O 657
 
2.2%
Other values (31) 2986
 
9.8%
Common
ValueCountFrequency (%)
316682
> 99.9%
‘ 9
 
< 0.1%
9 6
 
< 0.1%
. 5
 
< 0.1%
- 3
 
< 0.1%
, 3
 
< 0.1%
š 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 347133
> 99.9%
None 20
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
316682
91.2%
E 7823
 
2.3%
A 4075
 
1.2%
N 3028
 
0.9%
U 2898
 
0.8%
L 2676
 
0.8%
V 2393
 
0.7%
Z 2357
 
0.7%
I 809
 
0.2%
S 742
 
0.2%
Other values (35) 3650
 
1.1%
None
ValueCountFrequency (%)
à 10
50.0%
‘ 9
45.0%
š 1
 
5.0%

NPCEP11
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
1
234065 
2
85887 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters319952
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 234065
73.2%
2 85887
 
26.8%

Length

2024-05-06T23:51:43.485099image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:51:43.576617image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 234065
73.2%
2 85887
 
26.8%

Most occurring characters

ValueCountFrequency (%)
1 234065
73.2%
2 85887
 
26.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 319952
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 234065
73.2%
2 85887
 
26.8%

Most occurring scripts

ValueCountFrequency (%)
Common 319952
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 234065
73.2%
2 85887
 
26.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 319952
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 234065
73.2%
2 85887
 
26.8%

NPCEP13
Categorical

IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
234065 
2
56713 
3
24903 
4
 
2849
1
 
1422

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters319952
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
234065
73.2%
2 56713
 
17.7%
3 24903
 
7.8%
4 2849
 
0.9%
1 1422
 
0.4%

Length

2024-05-06T23:51:43.686041image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:51:43.791887image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2 56713
66.0%
3 24903
29.0%
4 2849
 
3.3%
1 1422
 
1.7%

Most occurring characters

ValueCountFrequency (%)
234065
73.2%
2 56713
 
17.7%
3 24903
 
7.8%
4 2849
 
0.9%
1 1422
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Space Separator 234065
73.2%
Decimal Number 85887
 
26.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 56713
66.0%
3 24903
29.0%
4 2849
 
3.3%
1 1422
 
1.7%
Space Separator
ValueCountFrequency (%)
234065
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 319952
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
234065
73.2%
2 56713
 
17.7%
3 24903
 
7.8%
4 2849
 
0.9%
1 1422
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 319952
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
234065
73.2%
2 56713
 
17.7%
3 24903
 
7.8%
4 2849
 
0.9%
1 1422
 
0.4%

NPCEP13A
Categorical

IMBALANCE 

Distinct34
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
295049 
11
 
12203
25
 
4743
15
 
1053
73
 
913
Other values (29)
 
5991

Length

Max length2
Median length1
Mean length1.0778336
Min length1

Characters and Unicode

Total characters344855
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
295049
92.2%
11 12203
 
3.8%
25 4743
 
1.5%
15 1053
 
0.3%
73 913
 
0.3%
05 602
 
0.2%
50 553
 
0.2%
68 528
 
0.2%
76 440
 
0.1%
08 406
 
0.1%
Other values (24) 3462
 
1.1%

Length

2024-05-06T23:51:43.906913image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
11 12203
49.0%
25 4743
 
19.0%
15 1053
 
4.2%
73 913
 
3.7%
05 602
 
2.4%
50 553
 
2.2%
68 528
 
2.1%
76 440
 
1.8%
08 406
 
1.6%
41 381
 
1.5%
Other values (23) 3081
 
12.4%

Most occurring characters

ValueCountFrequency (%)
295049
85.6%
1 26835
 
7.8%
5 7636
 
2.2%
2 5469
 
1.6%
7 2113
 
0.6%
0 1933
 
0.6%
3 1643
 
0.5%
6 1411
 
0.4%
8 1381
 
0.4%
4 1086
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Space Separator 295049
85.6%
Decimal Number 49806
 
14.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 26835
53.9%
5 7636
 
15.3%
2 5469
 
11.0%
7 2113
 
4.2%
0 1933
 
3.9%
3 1643
 
3.3%
6 1411
 
2.8%
8 1381
 
2.8%
4 1086
 
2.2%
9 299
 
0.6%
Space Separator
ValueCountFrequency (%)
295049
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 344855
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
295049
85.6%
1 26835
 
7.8%
5 7636
 
2.2%
2 5469
 
1.6%
7 2113
 
0.6%
0 1933
 
0.6%
3 1643
 
0.5%
6 1411
 
0.4%
8 1381
 
0.4%
4 1086
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 344855
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
295049
85.6%
1 26835
 
7.8%
5 7636
 
2.2%
2 5469
 
1.6%
7 2113
 
0.6%
0 1933
 
0.6%
3 1643
 
0.5%
6 1411
 
0.4%
8 1381
 
0.4%
4 1086
 
0.3%
Distinct794
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
2024-05-06T23:51:44.168738image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length5
Median length1
Mean length1.3113342
Min length1

Characters and Unicode

Total characters419564
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique159 ?
Unique (%)< 0.1%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
11001 12204
49.0%
05001 373
 
1.5%
08001 330
 
1.3%
50001 320
 
1.3%
73001 302
 
1.2%
25126 291
 
1.2%
25899 267
 
1.1%
76001 256
 
1.0%
68001 252
 
1.0%
54001 246
 
1.0%
Other values (783) 10062
40.4%
2024-05-06T23:51:44.579863image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
295049
70.3%
1 45398
 
10.8%
0 36333
 
8.7%
5 10232
 
2.4%
2 8304
 
2.0%
7 5440
 
1.3%
3 4346
 
1.0%
8 4217
 
1.0%
6 4128
 
1.0%
4 3312
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Space Separator 295049
70.3%
Decimal Number 124515
29.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 45398
36.5%
0 36333
29.2%
5 10232
 
8.2%
2 8304
 
6.7%
7 5440
 
4.4%
3 4346
 
3.5%
8 4217
 
3.4%
6 4128
 
3.3%
4 3312
 
2.7%
9 2805
 
2.3%
Space Separator
ValueCountFrequency (%)
295049
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 419564
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
295049
70.3%
1 45398
 
10.8%
0 36333
 
8.7%
5 10232
 
2.4%
2 8304
 
2.0%
7 5440
 
1.3%
3 4346
 
1.0%
8 4217
 
1.0%
6 4128
 
1.0%
4 3312
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 419564
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
295049
70.3%
1 45398
 
10.8%
0 36333
 
8.7%
5 10232
 
2.4%
2 8304
 
2.0%
7 5440
 
1.3%
3 4346
 
1.0%
8 4217
 
1.0%
6 4128
 
1.0%
4 3312
 
0.8%
Distinct137
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
2024-05-06T23:51:44.759848image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length35
Median length1
Mean length1.0686259
Min length1

Characters and Unicode

Total characters341909
Distinct characters46
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique77 ?
Unique (%)< 0.1%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
venezuela 1957
64.8%
estados 114
 
3.8%
unidos 112
 
3.7%
espana 104
 
3.4%
argentina 55
 
1.8%
ecuador 50
 
1.7%
mexico 41
 
1.4%
francia 38
 
1.3%
brasil 37
 
1.2%
colombia 36
 
1.2%
Other values (133) 476
 
15.8%
2024-05-06T23:51:45.064201image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
317274
92.8%
E 6495
 
1.9%
A 3282
 
1.0%
N 2525
 
0.7%
U 2333
 
0.7%
L 2237
 
0.7%
V 1994
 
0.6%
Z 1992
 
0.6%
S 610
 
0.2%
I 608
 
0.2%
Other values (36) 2559
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Space Separator 317274
92.8%
Uppercase Letter 24573
 
7.2%
Lowercase Letter 44
 
< 0.1%
Decimal Number 6
 
< 0.1%
Control 5
 
< 0.1%
Other Punctuation 4
 
< 0.1%
Dash Punctuation 3
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 6495
26.4%
A 3282
13.4%
N 2525
 
10.3%
U 2333
 
9.5%
L 2237
 
9.1%
V 1994
 
8.1%
Z 1992
 
8.1%
S 610
 
2.5%
I 608
 
2.5%
O 488
 
2.0%
Other values (17) 2009
 
8.2%
Lowercase Letter
ValueCountFrequency (%)
a 9
20.5%
t 4
9.1%
d 4
9.1%
e 4
9.1%
o 4
9.1%
s 4
9.1%
i 4
9.1%
n 3
 
6.8%
l 3
 
6.8%
u 3
 
6.8%
Other values (2) 2
 
4.5%
Other Punctuation
ValueCountFrequency (%)
? 2
50.0%
. 1
25.0%
, 1
25.0%
Space Separator
ValueCountFrequency (%)
317274
100.0%
Decimal Number
ValueCountFrequency (%)
9 6
100.0%
Control
ValueCountFrequency (%)
‘ 5
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 317292
92.8%
Latin 24617
 
7.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 6495
26.4%
A 3282
13.3%
N 2525
 
10.3%
U 2333
 
9.5%
L 2237
 
9.1%
V 1994
 
8.1%
Z 1992
 
8.1%
S 610
 
2.5%
I 608
 
2.5%
O 488
 
2.0%
Other values (29) 2053
 
8.3%
Common
ValueCountFrequency (%)
317274
> 99.9%
9 6
 
< 0.1%
‘ 5
 
< 0.1%
- 3
 
< 0.1%
? 2
 
< 0.1%
. 1
 
< 0.1%
, 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 341899
> 99.9%
None 10
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
317274
92.8%
E 6495
 
1.9%
A 3282
 
1.0%
N 2525
 
0.7%
U 2333
 
0.7%
L 2237
 
0.7%
V 1994
 
0.6%
Z 1992
 
0.6%
S 610
 
0.2%
I 608
 
0.2%
Other values (34) 2549
 
0.7%
None
ValueCountFrequency (%)
‘ 5
50.0%
à 5
50.0%

NPCEP14
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
238336 
1
76482 
2
 
5134

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters319952
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
238336
74.5%
1 76482
 
23.9%
2 5134
 
1.6%

Length

2024-05-06T23:51:45.206853image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:51:45.305354image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 76482
93.7%
2 5134
 
6.3%

Most occurring characters

ValueCountFrequency (%)
238336
74.5%
1 76482
 
23.9%
2 5134
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Space Separator 238336
74.5%
Decimal Number 81616
 
25.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 76482
93.7%
2 5134
 
6.3%
Space Separator
ValueCountFrequency (%)
238336
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 319952
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
238336
74.5%
1 76482
 
23.9%
2 5134
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 319952
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
238336
74.5%
1 76482
 
23.9%
2 5134
 
1.6%

NPCEP15
Categorical

IMBALANCE 

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
295049 
1
 
10903
11
 
4043
2
 
2629
4
 
2143
Other values (8)
 
5185

Length

Max length2
Median length1
Mean length1.0174214
Min length1

Characters and Unicode

Total characters325526
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
295049
92.2%
1 10903
 
3.4%
11 4043
 
1.3%
2 2629
 
0.8%
4 2143
 
0.7%
8 1131
 
0.4%
10 1072
 
0.3%
6 912
 
0.3%
3 695
 
0.2%
7 689
 
0.2%
Other values (3) 686
 
0.2%

Length

2024-05-06T23:51:45.405893image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1 10903
43.8%
11 4043
 
16.2%
2 2629
 
10.6%
4 2143
 
8.6%
8 1131
 
4.5%
10 1072
 
4.3%
6 912
 
3.7%
3 695
 
2.8%
7 689
 
2.8%
12 459
 
1.8%
Other values (2) 227
 
0.9%

Most occurring characters

ValueCountFrequency (%)
295049
90.6%
1 20520
 
6.3%
2 3088
 
0.9%
4 2143
 
0.7%
8 1131
 
0.3%
0 1072
 
0.3%
6 912
 
0.3%
3 695
 
0.2%
7 689
 
0.2%
9 196
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator 295049
90.6%
Decimal Number 30477
 
9.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 20520
67.3%
2 3088
 
10.1%
4 2143
 
7.0%
8 1131
 
3.7%
0 1072
 
3.5%
6 912
 
3.0%
3 695
 
2.3%
7 689
 
2.3%
9 196
 
0.6%
5 31
 
0.1%
Space Separator
ValueCountFrequency (%)
295049
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 325526
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
295049
90.6%
1 20520
 
6.3%
2 3088
 
0.9%
4 2143
 
0.7%
8 1131
 
0.3%
0 1072
 
0.3%
6 912
 
0.3%
3 695
 
0.2%
7 689
 
0.2%
9 196
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 325526
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
295049
90.6%
1 20520
 
6.3%
2 3088
 
0.9%
4 2143
 
0.7%
8 1131
 
0.3%
0 1072
 
0.3%
6 912
 
0.3%
3 695
 
0.2%
7 689
 
0.2%
9 196
 
0.1%

NPCEP16A
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
311868 
1
 
8084

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters319952
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
311868
97.5%
1 8084
 
2.5%

Length

2024-05-06T23:51:45.514742image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:51:45.603675image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 8084
100.0%

Most occurring characters

ValueCountFrequency (%)
311868
97.5%
1 8084
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Space Separator 311868
97.5%
Decimal Number 8084
 
2.5%

Most frequent character per category

Space Separator
ValueCountFrequency (%)
311868
100.0%
Decimal Number
ValueCountFrequency (%)
1 8084
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 319952
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
311868
97.5%
1 8084
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 319952
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
311868
97.5%
1 8084
 
2.5%

NPCEP16B
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
309697 
1
 
10255

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters319952
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
309697
96.8%
1 10255
 
3.2%

Length

2024-05-06T23:51:45.701349image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:51:45.787729image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 10255
100.0%

Most occurring characters

ValueCountFrequency (%)
309697
96.8%
1 10255
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Space Separator 309697
96.8%
Decimal Number 10255
 
3.2%

Most frequent character per category

Space Separator
ValueCountFrequency (%)
309697
100.0%
Decimal Number
ValueCountFrequency (%)
1 10255
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 319952
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
309697
96.8%
1 10255
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 319952
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
309697
96.8%
1 10255
 
3.2%

NPCEP16C
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
318425 
1
 
1527

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters319952
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
318425
99.5%
1 1527
 
0.5%

Length

2024-05-06T23:51:45.884312image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:51:45.972034image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 1527
100.0%

Most occurring characters

ValueCountFrequency (%)
318425
99.5%
1 1527
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Space Separator 318425
99.5%
Decimal Number 1527
 
0.5%

Most frequent character per category

Space Separator
ValueCountFrequency (%)
318425
100.0%
Decimal Number
ValueCountFrequency (%)
1 1527
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 319952
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
318425
99.5%
1 1527
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 319952
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
318425
99.5%
1 1527
 
0.5%

NPCEP16D
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
317245 
1
 
2707

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters319952
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
317245
99.2%
1 2707
 
0.8%

Length

2024-05-06T23:51:46.066236image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:51:46.160970image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 2707
100.0%

Most occurring characters

ValueCountFrequency (%)
317245
99.2%
1 2707
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Space Separator 317245
99.2%
Decimal Number 2707
 
0.8%

Most frequent character per category

Space Separator
ValueCountFrequency (%)
317245
100.0%
Decimal Number
ValueCountFrequency (%)
1 2707
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 319952
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
317245
99.2%
1 2707
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 319952
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
317245
99.2%
1 2707
 
0.8%

NPCEP16E
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
319694 
1
 
258

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters319952
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
319694
99.9%
1 258
 
0.1%

Length

2024-05-06T23:51:46.262526image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:51:46.354637image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 258
100.0%

Most occurring characters

ValueCountFrequency (%)
319694
99.9%
1 258
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator 319694
99.9%
Decimal Number 258
 
0.1%

Most frequent character per category

Space Separator
ValueCountFrequency (%)
319694
100.0%
Decimal Number
ValueCountFrequency (%)
1 258
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 319952
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
319694
99.9%
1 258
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 319952
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
319694
99.9%
1 258
 
0.1%

NPCEP16F
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
319778 
1
 
174

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters319952
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
319778
99.9%
1 174
 
0.1%

Length

2024-05-06T23:51:46.449803image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:51:46.535369image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 174
100.0%

Most occurring characters

ValueCountFrequency (%)
319778
99.9%
1 174
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator 319778
99.9%
Decimal Number 174
 
0.1%

Most frequent character per category

Space Separator
ValueCountFrequency (%)
319778
100.0%
Decimal Number
ValueCountFrequency (%)
1 174
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 319952
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
319778
99.9%
1 174
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 319952
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
319778
99.9%
1 174
 
0.1%

NPCEP16G
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
319902 
1
 
50

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters319952
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
319902
> 99.9%
1 50
 
< 0.1%

Length

2024-05-06T23:51:46.626106image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:51:46.715691image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 50
100.0%

Most occurring characters

ValueCountFrequency (%)
319902
> 99.9%
1 50
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator 319902
> 99.9%
Decimal Number 50
 
< 0.1%

Most frequent character per category

Space Separator
ValueCountFrequency (%)
319902
100.0%
Decimal Number
ValueCountFrequency (%)
1 50
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 319952
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
319902
> 99.9%
1 50
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 319952
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
319902
> 99.9%
1 50
 
< 0.1%

NPCEP16H
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
319876 
1
 
76

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters319952
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
319876
> 99.9%
1 76
 
< 0.1%

Length

2024-05-06T23:51:46.812274image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:51:46.900116image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 76
100.0%

Most occurring characters

ValueCountFrequency (%)
319876
> 99.9%
1 76
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator 319876
> 99.9%
Decimal Number 76
 
< 0.1%

Most frequent character per category

Space Separator
ValueCountFrequency (%)
319876
100.0%
Decimal Number
ValueCountFrequency (%)
1 76
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 319952
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
319876
> 99.9%
1 76
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 319952
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
319876
> 99.9%
1 76
 
< 0.1%

NPCEP16I
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
319942 
1
 
10

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters319952
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
319942
> 99.9%
1 10
 
< 0.1%

Length

2024-05-06T23:51:47.001269image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:51:47.089137image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 10
100.0%

Most occurring characters

ValueCountFrequency (%)
319942
> 99.9%
1 10
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator 319942
> 99.9%
Decimal Number 10
 
< 0.1%

Most frequent character per category

Space Separator
ValueCountFrequency (%)
319942
100.0%
Decimal Number
ValueCountFrequency (%)
1 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 319952
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
319942
> 99.9%
1 10
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 319952
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
319942
> 99.9%
1 10
 
< 0.1%

NPCEP16J
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
319818 
1
 
134

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters319952
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
319818
> 99.9%
1 134
 
< 0.1%

Length

2024-05-06T23:51:47.186946image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:51:47.286361image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 134
100.0%

Most occurring characters

ValueCountFrequency (%)
319818
> 99.9%
1 134
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator 319818
> 99.9%
Decimal Number 134
 
< 0.1%

Most frequent character per category

Space Separator
ValueCountFrequency (%)
319818
100.0%
Decimal Number
ValueCountFrequency (%)
1 134
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 319952
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
319818
> 99.9%
1 134
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 319952
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
319818
> 99.9%
1 134
 
< 0.1%

NPCEP16K
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
316003 
1
 
3949

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters319952
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
316003
98.8%
1 3949
 
1.2%

Length

2024-05-06T23:51:47.385226image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:51:47.471316image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 3949
100.0%

Most occurring characters

ValueCountFrequency (%)
316003
98.8%
1 3949
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Space Separator 316003
98.8%
Decimal Number 3949
 
1.2%

Most frequent character per category

Space Separator
ValueCountFrequency (%)
316003
100.0%
Decimal Number
ValueCountFrequency (%)
1 3949
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 319952
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
316003
98.8%
1 3949
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 319952
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
316003
98.8%
1 3949
 
1.2%

NPCEP16A1
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
1
249324 
45918 
2
 
24710

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters319952
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 249324
77.9%
45918
 
14.4%
2 24710
 
7.7%

Length

2024-05-06T23:51:47.561831image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:51:47.651444image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 249324
91.0%
2 24710
 
9.0%

Most occurring characters

ValueCountFrequency (%)
1 249324
77.9%
45918
 
14.4%
2 24710
 
7.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 274034
85.6%
Space Separator 45918
 
14.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 249324
91.0%
2 24710
 
9.0%
Space Separator
ValueCountFrequency (%)
45918
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 319952
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 249324
77.9%
45918
 
14.4%
2 24710
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 319952
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 249324
77.9%
45918
 
14.4%
2 24710
 
7.7%

NPCEP16AA
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
305250 
1
 
10128
2
 
4574

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters319952
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
305250
95.4%
1 10128
 
3.2%
2 4574
 
1.4%

Length

2024-05-06T23:51:47.761742image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:51:47.859274image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 10128
68.9%
2 4574
31.1%

Most occurring characters

ValueCountFrequency (%)
305250
95.4%
1 10128
 
3.2%
2 4574
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Space Separator 305250
95.4%
Decimal Number 14702
 
4.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 10128
68.9%
2 4574
31.1%
Space Separator
ValueCountFrequency (%)
305250
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 319952
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
305250
95.4%
1 10128
 
3.2%
2 4574
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 319952
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
305250
95.4%
1 10128
 
3.2%
2 4574
 
1.4%

NPCEP16AB
Categorical

IMBALANCE 

Distinct22
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
309824 
11
 
1273
8
 
1233
10
 
934
7
 
729
Other values (17)
 
5959

Length

Max length2
Median length1
Mean length1.0164431
Min length1

Characters and Unicode

Total characters325213
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
309824
96.8%
11 1273
 
0.4%
8 1233
 
0.4%
10 934
 
0.3%
7 729
 
0.2%
19 661
 
0.2%
9 606
 
0.2%
18 580
 
0.2%
4 547
 
0.2%
2 500
 
0.2%
Other values (12) 3065
 
1.0%

Length

2024-05-06T23:51:47.960104image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
11 1273
12.6%
8 1233
12.2%
10 934
 
9.2%
7 729
 
7.2%
19 661
 
6.5%
9 606
 
6.0%
18 580
 
5.7%
4 547
 
5.4%
2 500
 
4.9%
1 495
 
4.9%
Other values (11) 2570
25.4%

Most occurring characters

ValueCountFrequency (%)
309824
95.3%
1 6758
 
2.1%
8 1813
 
0.6%
9 1771
 
0.5%
0 953
 
0.3%
7 863
 
0.3%
2 825
 
0.3%
4 740
 
0.2%
6 738
 
0.2%
5 561
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Space Separator 309824
95.3%
Decimal Number 15389
 
4.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 6758
43.9%
8 1813
 
11.8%
9 1771
 
11.5%
0 953
 
6.2%
7 863
 
5.6%
2 825
 
5.4%
4 740
 
4.8%
6 738
 
4.8%
5 561
 
3.6%
3 367
 
2.4%
Space Separator
ValueCountFrequency (%)
309824
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 325213
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
309824
95.3%
1 6758
 
2.1%
8 1813
 
0.6%
9 1771
 
0.5%
0 953
 
0.3%
7 863
 
0.3%
2 825
 
0.3%
4 740
 
0.2%
6 738
 
0.2%
5 561
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 325213
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
309824
95.3%
1 6758
 
2.1%
8 1813
 
0.6%
9 1771
 
0.5%
0 953
 
0.3%
7 863
 
0.3%
2 825
 
0.3%
4 740
 
0.2%
6 738
 
0.2%
5 561
 
0.2%

NPCEP16B1
Categorical

IMBALANCE 

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
295242 
8
 
5859
1
 
3708
7
 
3706
4
 
3386
Other values (8)
 
8051

Length

Max length2
Median length1
Mean length1.018159
Min length1

Characters and Unicode

Total characters325762
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
295242
92.3%
8 5859
 
1.8%
1 3708
 
1.2%
7 3706
 
1.2%
4 3386
 
1.1%
11 3344
 
1.0%
10 1831
 
0.6%
2 987
 
0.3%
12 635
 
0.2%
3 464
 
0.1%
Other values (3) 790
 
0.2%

Length

2024-05-06T23:51:48.069856image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
8 5859
23.7%
1 3708
15.0%
7 3706
15.0%
4 3386
13.7%
11 3344
13.5%
10 1831
 
7.4%
2 987
 
4.0%
12 635
 
2.6%
3 464
 
1.9%
9 379
 
1.5%
Other values (2) 411
 
1.7%

Most occurring characters

ValueCountFrequency (%)
295242
90.6%
1 12862
 
3.9%
8 5859
 
1.8%
7 3706
 
1.1%
4 3386
 
1.0%
0 1831
 
0.6%
2 1622
 
0.5%
3 464
 
0.1%
9 379
 
0.1%
6 336
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator 295242
90.6%
Decimal Number 30520
 
9.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 12862
42.1%
8 5859
19.2%
7 3706
 
12.1%
4 3386
 
11.1%
0 1831
 
6.0%
2 1622
 
5.3%
3 464
 
1.5%
9 379
 
1.2%
6 336
 
1.1%
5 75
 
0.2%
Space Separator
ValueCountFrequency (%)
295242
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 325762
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
295242
90.6%
1 12862
 
3.9%
8 5859
 
1.8%
7 3706
 
1.1%
4 3386
 
1.0%
0 1831
 
0.6%
2 1622
 
0.5%
3 464
 
0.1%
9 379
 
0.1%
6 336
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 325762
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
295242
90.6%
1 12862
 
3.9%
8 5859
 
1.8%
7 3706
 
1.1%
4 3386
 
1.0%
0 1831
 
0.6%
2 1622
 
0.5%
3 464
 
0.1%
9 379
 
0.1%
6 336
 
0.1%

NPCEP17
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.9781405
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2024-05-06T23:51:48.162924image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q16
median6
Q36
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.28431428
Coefficient of variation (CV)0.047558983
Kurtosis266.34015
Mean5.9781405
Median Absolute Deviation (MAD)0
Skewness-15.908514
Sum1912718
Variance0.08083461
MonotonicityNot monotonic
2024-05-06T23:51:48.268793image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
6 316907
99.0%
5 1973
 
0.6%
1 856
 
0.3%
2 117
 
< 0.1%
3 75
 
< 0.1%
4 24
 
< 0.1%
ValueCountFrequency (%)
1 856
 
0.3%
2 117
 
< 0.1%
3 75
 
< 0.1%
4 24
 
< 0.1%
5 1973
 
0.6%
6 316907
99.0%
ValueCountFrequency (%)
6 316907
99.0%
5 1973
 
0.6%
4 24
 
< 0.1%
3 75
 
< 0.1%
2 117
 
< 0.1%
1 856
 
0.3%
Distinct103
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
2024-05-06T23:51:48.460844image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length6
Median length1
Mean length1.013377
Min length1

Characters and Unicode

Total characters324232
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40 ?
Unique (%)< 0.1%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
470_01 236
27.6%
999_01 97
 
11.3%
200_01 80
 
9.3%
470_02 61
 
7.1%
840_01 33
 
3.9%
500_03 29
 
3.4%
650_01 20
 
2.3%
340_01 18
 
2.1%
500_01 17
 
2.0%
560_01 15
 
1.8%
Other values (92) 250
29.2%
2024-05-06T23:51:49.062743image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
319096
98.4%
0 1798
 
0.6%
_ 856
 
0.3%
1 668
 
0.2%
4 395
 
0.1%
7 358
 
0.1%
9 315
 
0.1%
2 308
 
0.1%
5 140
 
< 0.1%
3 121
 
< 0.1%
Other values (2) 177
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator 319096
98.4%
Decimal Number 4280
 
1.3%
Connector Punctuation 856
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1798
42.0%
1 668
 
15.6%
4 395
 
9.2%
7 358
 
8.4%
9 315
 
7.4%
2 308
 
7.2%
5 140
 
3.3%
3 121
 
2.8%
8 104
 
2.4%
6 73
 
1.7%
Space Separator
ValueCountFrequency (%)
319096
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 856
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 324232
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
319096
98.4%
0 1798
 
0.6%
_ 856
 
0.3%
1 668
 
0.2%
4 395
 
0.1%
7 358
 
0.1%
9 315
 
0.1%
2 308
 
0.1%
5 140
 
< 0.1%
3 121
 
< 0.1%
Other values (2) 177
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 324232
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
319096
98.4%
0 1798
 
0.6%
_ 856
 
0.3%
1 668
 
0.2%
4 395
 
0.1%
7 358
 
0.1%
9 315
 
0.1%
2 308
 
0.1%
5 140
 
< 0.1%
3 121
 
< 0.1%
Other values (2) 177
 
0.1%

NPCEP19
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
318880 
2
 
745
1
 
327

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters319952
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
318880
99.7%
2 745
 
0.2%
1 327
 
0.1%

Length

2024-05-06T23:51:49.261787image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:51:49.378501image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2 745
69.5%
1 327
30.5%

Most occurring characters

ValueCountFrequency (%)
318880
99.7%
2 745
 
0.2%
1 327
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator 318880
99.7%
Decimal Number 1072
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 745
69.5%
1 327
30.5%
Space Separator
ValueCountFrequency (%)
318880
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 319952
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
318880
99.7%
2 745
 
0.2%
1 327
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 319952
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
318880
99.7%
2 745
 
0.2%
1 327
 
0.1%

NPCEP21
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
2
149135 
3
93523 
1
77294 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters319952
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row2
5th row3

Common Values

ValueCountFrequency (%)
2 149135
46.6%
3 93523
29.2%
1 77294
24.2%

Length

2024-05-06T23:51:49.498389image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:51:49.613956image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2 149135
46.6%
3 93523
29.2%
1 77294
24.2%

Most occurring characters

ValueCountFrequency (%)
2 149135
46.6%
3 93523
29.2%
1 77294
24.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 319952
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 149135
46.6%
3 93523
29.2%
1 77294
24.2%

Most occurring scripts

ValueCountFrequency (%)
Common 319952
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 149135
46.6%
3 93523
29.2%
1 77294
24.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 319952
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 149135
46.6%
3 93523
29.2%
1 77294
24.2%

NPCEP21A
Categorical

IMBALANCE 

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
242654 
1
61910 
2
 
11420
3
 
1806
4
 
1094
Other values (9)
 
1068

Length

Max length2
Median length1
Mean length1.0000344
Min length1

Characters and Unicode

Total characters319963
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row
2nd row
3rd row1
4th row
5th row

Common Values

ValueCountFrequency (%)
242654
75.8%
1 61910
 
19.3%
2 11420
 
3.6%
3 1806
 
0.6%
4 1094
 
0.3%
5 603
 
0.2%
6 252
 
0.1%
7 113
 
< 0.1%
8 55
 
< 0.1%
9 34
 
< 0.1%
Other values (4) 11
 
< 0.1%

Length

2024-05-06T23:51:49.816697image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1 61910
80.1%
2 11420
 
14.8%
3 1806
 
2.3%
4 1094
 
1.4%
5 603
 
0.8%
6 252
 
0.3%
7 113
 
0.1%
8 55
 
0.1%
9 34
 
< 0.1%
10 7
 
< 0.1%
Other values (3) 4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
242654
75.8%
1 61923
 
19.4%
2 11421
 
3.6%
3 1806
 
0.6%
4 1094
 
0.3%
5 604
 
0.2%
6 252
 
0.1%
7 113
 
< 0.1%
8 55
 
< 0.1%
9 34
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator 242654
75.8%
Decimal Number 77309
 
24.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 61923
80.1%
2 11421
 
14.8%
3 1806
 
2.3%
4 1094
 
1.4%
5 604
 
0.8%
6 252
 
0.3%
7 113
 
0.1%
8 55
 
0.1%
9 34
 
< 0.1%
0 7
 
< 0.1%
Space Separator
ValueCountFrequency (%)
242654
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 319963
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
242654
75.8%
1 61923
 
19.4%
2 11421
 
3.6%
3 1806
 
0.6%
4 1094
 
0.3%
5 604
 
0.2%
6 252
 
0.1%
7 113
 
< 0.1%
8 55
 
< 0.1%
9 34
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 319963
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
242654
75.8%
1 61923
 
19.4%
2 11421
 
3.6%
3 1806
 
0.6%
4 1094
 
0.3%
5 604
 
0.2%
6 252
 
0.1%
7 113
 
< 0.1%
8 55
 
< 0.1%
9 34
 
< 0.1%

NPCEP22
Categorical

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
77294 
2
76824 
99
45019 
4
38640 
1
28756 
Other values (7)
53419 

Length

Max length2
Median length1
Mean length1.2092189
Min length1

Characters and Unicode

Total characters386892
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row
4th row1
5th row1

Common Values

ValueCountFrequency (%)
77294
24.2%
2 76824
24.0%
99 45019
14.1%
4 38640
12.1%
1 28756
 
9.0%
10 21921
 
6.9%
8 13574
 
4.2%
3 8085
 
2.5%
6 5112
 
1.6%
9 2123
 
0.7%
Other values (2) 2604
 
0.8%

Length

2024-05-06T23:51:49.985337image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2 76824
31.7%
99 45019
18.6%
4 38640
15.9%
1 28756
 
11.9%
10 21921
 
9.0%
8 13574
 
5.6%
3 8085
 
3.3%
6 5112
 
2.1%
9 2123
 
0.9%
5 1403
 
0.6%

Most occurring characters

ValueCountFrequency (%)
9 92161
23.8%
77294
20.0%
2 76824
19.9%
1 50677
13.1%
4 38640
10.0%
0 21921
 
5.7%
8 13574
 
3.5%
3 8085
 
2.1%
6 5112
 
1.3%
5 1403
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 309598
80.0%
Space Separator 77294
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 92161
29.8%
2 76824
24.8%
1 50677
16.4%
4 38640
12.5%
0 21921
 
7.1%
8 13574
 
4.4%
3 8085
 
2.6%
6 5112
 
1.7%
5 1403
 
0.5%
7 1201
 
0.4%
Space Separator
ValueCountFrequency (%)
77294
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 386892
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 92161
23.8%
77294
20.0%
2 76824
19.9%
1 50677
13.1%
4 38640
10.0%
0 21921
 
5.7%
8 13574
 
3.5%
3 8085
 
2.1%
6 5112
 
1.3%
5 1403
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 386892
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 92161
23.8%
77294
20.0%
2 76824
19.9%
1 50677
13.1%
4 38640
10.0%
0 21921
 
5.7%
8 13574
 
3.5%
3 8085
 
2.1%
6 5112
 
1.3%
5 1403
 
0.4%

NPCEP22A
Categorical

IMBALANCE 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
278384 
3
 
14521
2
 
13257
4
 
7191
1
 
5302
Other values (2)
 
1297

Length

Max length2
Median length1
Mean length1.0000031
Min length1

Characters and Unicode

Total characters319953
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row3
2nd row2
3rd row
4th row4
5th row1

Common Values

ValueCountFrequency (%)
278384
87.0%
3 14521
 
4.5%
2 13257
 
4.1%
4 7191
 
2.2%
1 5302
 
1.7%
5 1296
 
0.4%
10 1
 
< 0.1%

Length

2024-05-06T23:51:50.132589image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:51:50.259335image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
3 14521
34.9%
2 13257
31.9%
4 7191
17.3%
1 5302
 
12.8%
5 1296
 
3.1%
10 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
278384
87.0%
3 14521
 
4.5%
2 13257
 
4.1%
4 7191
 
2.2%
1 5303
 
1.7%
5 1296
 
0.4%
0 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator 278384
87.0%
Decimal Number 41569
 
13.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 14521
34.9%
2 13257
31.9%
4 7191
17.3%
1 5303
 
12.8%
5 1296
 
3.1%
0 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
278384
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 319953
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
278384
87.0%
3 14521
 
4.5%
2 13257
 
4.1%
4 7191
 
2.2%
1 5303
 
1.7%
5 1296
 
0.4%
0 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 319953
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
278384
87.0%
3 14521
 
4.5%
2 13257
 
4.1%
4 7191
 
2.2%
1 5303
 
1.7%
5 1296
 
0.4%
0 1
 
< 0.1%

NPCEP24
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
2
127967 
1
124708 
3
67277 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters319952
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 127967
40.0%
1 124708
39.0%
3 67277
21.0%

Length

2024-05-06T23:51:50.396200image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:51:50.513213image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2 127967
40.0%
1 124708
39.0%
3 67277
21.0%

Most occurring characters

ValueCountFrequency (%)
2 127967
40.0%
1 124708
39.0%
3 67277
21.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 319952
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 127967
40.0%
1 124708
39.0%
3 67277
21.0%

Most occurring scripts

ValueCountFrequency (%)
Common 319952
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 127967
40.0%
1 124708
39.0%
3 67277
21.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 319952
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 127967
40.0%
1 124708
39.0%
3 67277
21.0%

NPCEP24A
Categorical

IMBALANCE 

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
195240 
2
68542 
1
44105 
3
 
6180
4
 
3053
Other values (10)
 
2832

Length

Max length2
Median length1
Mean length1.0001344
Min length1

Characters and Unicode

Total characters319995
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row
2nd row
3rd row2
4th row
5th row

Common Values

ValueCountFrequency (%)
195240
61.0%
2 68542
 
21.4%
1 44105
 
13.8%
3 6180
 
1.9%
4 3053
 
1.0%
5 1666
 
0.5%
6 717
 
0.2%
7 253
 
0.1%
8 100
 
< 0.1%
9 53
 
< 0.1%
Other values (5) 43
 
< 0.1%

Length

2024-05-06T23:51:50.647096image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2 68542
55.0%
1 44105
35.4%
3 6180
 
5.0%
4 3053
 
2.4%
5 1666
 
1.3%
6 717
 
0.6%
7 253
 
0.2%
8 100
 
0.1%
9 53
 
< 0.1%
10 24
 
< 0.1%
Other values (4) 19
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
195240
61.0%
2 68546
 
21.4%
1 44160
 
13.8%
3 6182
 
1.9%
4 3053
 
1.0%
5 1666
 
0.5%
6 718
 
0.2%
7 253
 
0.1%
8 100
 
< 0.1%
9 53
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator 195240
61.0%
Decimal Number 124755
39.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 68546
54.9%
1 44160
35.4%
3 6182
 
5.0%
4 3053
 
2.4%
5 1666
 
1.3%
6 718
 
0.6%
7 253
 
0.2%
8 100
 
0.1%
9 53
 
< 0.1%
0 24
 
< 0.1%
Space Separator
ValueCountFrequency (%)
195240
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 319995
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
195240
61.0%
2 68546
 
21.4%
1 44160
 
13.8%
3 6182
 
1.9%
4 3053
 
1.0%
5 1666
 
0.5%
6 718
 
0.2%
7 253
 
0.1%
8 100
 
< 0.1%
9 53
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 319995
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
195240
61.0%
2 68546
 
21.4%
1 44160
 
13.8%
3 6182
 
1.9%
4 3053
 
1.0%
5 1666
 
0.5%
6 718
 
0.2%
7 253
 
0.1%
8 100
 
< 0.1%
9 53
 
< 0.1%

NPCEP25
Categorical

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
124708 
2
68734 
4
29534 
99
27876 
1
27267 
Other values (7)
41833 

Length

Max length2
Median length1
Mean length1.1541231
Min length1

Characters and Unicode

Total characters369264
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row10
3rd row
4th row3
5th row1

Common Values

ValueCountFrequency (%)
124708
39.0%
2 68734
21.5%
4 29534
 
9.2%
99 27876
 
8.7%
1 27267
 
8.5%
10 21436
 
6.7%
8 7094
 
2.2%
3 6607
 
2.1%
6 3822
 
1.2%
9 1292
 
0.4%
Other values (2) 1582
 
0.5%

Length

2024-05-06T23:51:50.775042image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2 68734
35.2%
4 29534
15.1%
99 27876
14.3%
1 27267
 
14.0%
10 21436
 
11.0%
8 7094
 
3.6%
3 6607
 
3.4%
6 3822
 
2.0%
9 1292
 
0.7%
5 1021
 
0.5%

Most occurring characters

ValueCountFrequency (%)
124708
33.8%
2 68734
18.6%
9 57044
15.4%
1 48703
 
13.2%
4 29534
 
8.0%
0 21436
 
5.8%
8 7094
 
1.9%
3 6607
 
1.8%
6 3822
 
1.0%
5 1021
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 244556
66.2%
Space Separator 124708
33.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 68734
28.1%
9 57044
23.3%
1 48703
19.9%
4 29534
12.1%
0 21436
 
8.8%
8 7094
 
2.9%
3 6607
 
2.7%
6 3822
 
1.6%
5 1021
 
0.4%
7 561
 
0.2%
Space Separator
ValueCountFrequency (%)
124708
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 369264
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
124708
33.8%
2 68734
18.6%
9 57044
15.4%
1 48703
 
13.2%
4 29534
 
8.0%
0 21436
 
5.8%
8 7094
 
1.9%
3 6607
 
1.8%
6 3822
 
1.0%
5 1021
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 369264
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
124708
33.8%
2 68734
18.6%
9 57044
15.4%
1 48703
 
13.2%
4 29534
 
8.0%
0 21436
 
5.8%
8 7094
 
1.9%
3 6607
 
1.8%
6 3822
 
1.0%
5 1021
 
0.3%

NPCEP25A
Categorical

IMBALANCE 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
283204 
3
 
12676
2
 
11744
4
 
6310
1
 
5109

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters319952
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row4
5th row3

Common Values

ValueCountFrequency (%)
283204
88.5%
3 12676
 
4.0%
2 11744
 
3.7%
4 6310
 
2.0%
1 5109
 
1.6%
5 909
 
0.3%

Length

2024-05-06T23:51:50.921733image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:51:51.039732image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
3 12676
34.5%
2 11744
32.0%
4 6310
17.2%
1 5109
13.9%
5 909
 
2.5%

Most occurring characters

ValueCountFrequency (%)
283204
88.5%
3 12676
 
4.0%
2 11744
 
3.7%
4 6310
 
2.0%
1 5109
 
1.6%
5 909
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Space Separator 283204
88.5%
Decimal Number 36748
 
11.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 12676
34.5%
2 11744
32.0%
4 6310
17.2%
1 5109
13.9%
5 909
 
2.5%
Space Separator
ValueCountFrequency (%)
283204
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 319952
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
283204
88.5%
3 12676
 
4.0%
2 11744
 
3.7%
4 6310
 
2.0%
1 5109
 
1.6%
5 909
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 319952
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
283204
88.5%
3 12676
 
4.0%
2 11744
 
3.7%
4 6310
 
2.0%
1 5109
 
1.6%
5 909
 
0.3%

NPCEP27
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
1
242829 
75274 
2
 
1385
3
 
464

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters319952
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 242829
75.9%
75274
 
23.5%
2 1385
 
0.4%
3 464
 
0.1%

Length

2024-05-06T23:51:51.195494image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:51:51.338708image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 242829
99.2%
2 1385
 
0.6%
3 464
 
0.2%

Most occurring characters

ValueCountFrequency (%)
1 242829
75.9%
75274
 
23.5%
2 1385
 
0.4%
3 464
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 244678
76.5%
Space Separator 75274
 
23.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 242829
99.2%
2 1385
 
0.6%
3 464
 
0.2%
Space Separator
ValueCountFrequency (%)
75274
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 319952
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 242829
75.9%
75274
 
23.5%
2 1385
 
0.4%
3 464
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 319952
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 242829
75.9%
75274
 
23.5%
2 1385
 
0.4%
3 464
 
0.1%

NPCEP26
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
1
134105 
2
110461 
75274 
3
 
112

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters319952
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 134105
41.9%
2 110461
34.5%
75274
23.5%
3 112
 
< 0.1%

Length

2024-05-06T23:51:51.506522image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:51:51.625432image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 134105
54.8%
2 110461
45.1%
3 112
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1 134105
41.9%
2 110461
34.5%
75274
23.5%
3 112
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 244678
76.5%
Space Separator 75274
 
23.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 134105
54.8%
2 110461
45.1%
3 112
 
< 0.1%
Space Separator
ValueCountFrequency (%)
75274
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 319952
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 134105
41.9%
2 110461
34.5%
75274
23.5%
3 112
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 319952
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 134105
41.9%
2 110461
34.5%
75274
23.5%
3 112
 
< 0.1%

NPCEP5A
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
2
169270 
1
150682 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters319952
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row2
4th row1
5th row2

Common Values

ValueCountFrequency (%)
2 169270
52.9%
1 150682
47.1%

Length

2024-05-06T23:51:51.786438image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:51:51.918978image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2 169270
52.9%
1 150682
47.1%

Most occurring characters

ValueCountFrequency (%)
2 169270
52.9%
1 150682
47.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 319952
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 169270
52.9%
1 150682
47.1%

Most occurring scripts

ValueCountFrequency (%)
Common 319952
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 169270
52.9%
1 150682
47.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 319952
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 169270
52.9%
1 150682
47.1%

FEX_C
Text

Distinct33342
Distinct (%)10.4%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
2024-05-06T23:51:52.159390image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length12
Median length12
Mean length11.08199
Min length1

Characters and Unicode

Total characters3545705
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique327 ?
Unique (%)0.1%

Sample

1st row16,604442041
2nd row16,604442041
3rd row16,604442041
4th row26,046357048
5th row26,046357048
ValueCountFrequency (%)
1 23508
 
7.3%
4,1739956106 1420
 
0.4%
5,8873957278 1186
 
0.4%
3,3593691726 766
 
0.2%
12,432040251 720
 
0.2%
9,2536882129 689
 
0.2%
17,68478686 686
 
0.2%
24,278983997 615
 
0.2%
30,327762873 608
 
0.2%
1,9622544989 547
 
0.2%
Other values (33332) 289207
90.4%
2024-05-06T23:51:52.624818image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 380078
10.7%
2 353237
10.0%
3 342151
9.6%
4 331896
9.4%
5 326012
9.2%
6 321031
9.1%
7 314468
8.9%
8 308028
8.7%
9 306766
8.7%
, 296444
8.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3249261
91.6%
Other Punctuation 296444
 
8.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 380078
11.7%
2 353237
10.9%
3 342151
10.5%
4 331896
10.2%
5 326012
10.0%
6 321031
9.9%
7 314468
9.7%
8 308028
9.5%
9 306766
9.4%
0 265594
8.2%
Other Punctuation
ValueCountFrequency (%)
, 296444
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3545705
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 380078
10.7%
2 353237
10.0%
3 342151
9.6%
4 331896
9.4%
5 326012
9.2%
6 321031
9.1%
7 314468
8.9%
8 308028
8.7%
9 306766
8.7%
, 296444
8.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3545705
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 380078
10.7%
2 353237
10.0%
3 342151
9.6%
4 331896
9.4%
5 326012
9.2%
6 321031
9.1%
7 314468
8.9%
8 308028
8.7%
9 306766
8.7%
, 296444
8.4%

Interactions

2024-05-06T23:51:31.597699image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:51:24.219647image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:51:25.220451image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:51:26.184968image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:51:27.220157image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:51:28.275953image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:51:29.307367image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:51:30.553811image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:51:31.724503image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:51:24.348739image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:51:25.343153image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:51:26.308229image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:51:27.352841image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:51:28.404611image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:51:29.454493image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:51:30.687874image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:51:31.846507image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:51:24.464922image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:51:25.461495image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:51:26.422287image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:51:27.478599image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:51:28.529676image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:51:29.594346image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:51:30.814117image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:51:31.961900image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:51:24.584648image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:51:25.579829image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:51:26.537360image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:51:27.599242image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:51:28.648774image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:51:29.872724image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:51:30.938945image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:51:32.088826image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:51:24.717809image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:51:25.708829image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:51:26.682667image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:51:27.743683image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:51:28.786968image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:51:30.008623image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:51:31.085807image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:51:32.218218image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:51:24.842866image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:51:25.831071image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:51:26.826562image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:51:27.883452image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:51:28.924881image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:51:30.140512image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:51:31.228874image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:51:32.335203image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:51:24.968588image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:51:25.944667image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:51:26.967022image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:51:28.016932image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:51:29.050447image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:51:30.282960image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:51:31.350583image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:51:32.462021image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:51:25.097997image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:51:26.066267image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:51:27.101321image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:51:28.145629image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:51:29.176158image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:51:30.418042image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:51:31.472872image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Missing values

2024-05-06T23:51:33.085918image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-06T23:51:34.657178image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

DIRECTORIO_PERDIRECTORIO_HOGDIRECTORIOSECUENCIA_PORDENNPCEP4NPCEP5NPCEP6NPCEP7NPCEP8NPCEP8ANPCEP9NPCEP9ANPCEP9BNPCEP10NPCEP11ANPCEP11AANPCEP11ABNPCEP11ACNPCEP11NPCEP13NPCEP13ANPCEP13BNPCEP13CNPCEP14NPCEP15NPCEP16ANPCEP16BNPCEP16CNPCEP16DNPCEP16ENPCEP16FNPCEP16GNPCEP16HNPCEP16INPCEP16JNPCEP16KNPCEP16A1NPCEP16AANPCEP16ABNPCEP16B1NPCEP17NPCEP18NPCEP19NPCEP21NPCEP21ANPCEP22NPCEP22ANPCEP24NPCEP24ANPCEP25NPCEP25ANPCEP27NPCEP26NPCEP5AFEX_C
010100011101000110100011561161231515842221515842221162132212116,604442041
110100012101000110100012482261131515842212211621221011216,604442041
21010001310100011010001322235211116111211216,604442041
310100111101001110100111421161221111621423412126,046357048
410100112101001110100112432261121111631121311226,046357048
51010011310100111010011320235211116111211226,046357048
610100114101001110100114123211161112226,046357048
7101002111010021101002116521331515244112211631231211213,840826089
81010021210100211010021233135211116361112113,840826089
910100311101003110100311641161231515660221515660222163103101217,0111108805
DIRECTORIO_PERDIRECTORIO_HOGDIRECTORIOSECUENCIA_PORDENNPCEP4NPCEP5NPCEP6NPCEP7NPCEP8NPCEP8ANPCEP9NPCEP9ANPCEP9BNPCEP10NPCEP11ANPCEP11AANPCEP11ABNPCEP11ACNPCEP11NPCEP13NPCEP13ANPCEP13BNPCEP13CNPCEP14NPCEP15NPCEP16ANPCEP16BNPCEP16CNPCEP16DNPCEP16ENPCEP16FNPCEP16GNPCEP16HNPCEP16INPCEP16JNPCEP16KNPCEP16A1NPCEP16AANPCEP16ABNPCEP16B1NPCEP17NPCEP18NPCEP19NPCEP21NPCEP21ANPCEP22NPCEP22ANPCEP24NPCEP24ANPCEP25NPCEP25ANPCEP27NPCEP26NPCEP5AFEX_C
319942317835133178351317835132213521111611121211
319943317835143178351317835141813522111611121211
3199443178441131784413178441139112122211162102101211
3199453178441231784413178441239222112211162102101121
319946317859113178591317859115921321215150871163113111121
319947317874113178741317874112511521111622221211
3199483178811131788113178811132115221116310121111
31994931788112317881131788112602532211163103101121
3199503178851131788513178851151112122211162102101211
3199513178851231788513178851247222112211162102101121